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USING A MULTI-LEVEL APPROACH FOR THE ANALYSIS OF VARIANTS IN INDIVIDUAL GENOMES
Autores:
GINÉS ALMAGRO
,
FRANCISCO GARCÍA SÁNCHEZ
, MARÍA EUGENIA DE LA MORENA BARRIO,
JAVIER CORRAL DE LA CALLE
,
JESUALDO TOMÁS FERNÁNDEZ BREIS
,
Grupos de investigación:
[GI/IMIB/E170/2011] TECNOLOGÍAS DE MODELADO, PROCESAMIENTO Y GESTIÓN DEL CONOCIMIENTO
[GI/IMIB/C001/2011] HEMATOLOGÍA Y ONCOLOGÍA MÉDICA CLÍNICO-EXPERIMENTAL
Comunicación:
Antecedentes:
Personalized medicine pursues finding the association between the genotype of an individual and its phenotypical features, with the aim of improving early diagnosis processes, the efficiency of treatments and facilitating the discovery of new therapies. Providing personalized solutions require facing challenges such as the different nature of the phenotypes because of the functional and structural complexity of the genome, the huge genomic variability at both individual and population level, and the complexity of the interactions networks when combining genes, functional RNA, epigenetic factors and metabolic factors. Next generation sequencing (NGS) technologies are increasingly used in genomic medicine for purposes such as the annotation, interpretation and prioritization of genomic variants. The current NGS-based approaches have the limitation of analyzing variants on an individual basis, focusing mainly on content available in public databases about SNPs, coding regions and clinical significance.
Métodos:
Our method pursues the accurate and reliable quantification of the genotype-phenotype relation from the genomic variants identified by NGS technologies for an individual person. We have designed a multi-layer functional analysis based on structural models of genes, pathways and phenotypes, through the curated information collected from the different and public bioinformatics resources. A gene is structurally modeled as a series of functional regions (exons, UTRs, promoter, splicing sites, introns and regulatory elements). Each functional region may contain functional subregions (e.g. binding domain, sequences motifs, etc.), and each functional subregion may contain functional basic regions (e.g. binding residues, catalytic residues, etc.). Each structural component of the gene model is assigned a functional weight, which represents the role of each of them about the total functionality of a particular gene. The information about the variants is used to estimate both the functional implication of each single variant and the total functional impact of all the variants for each particular gene. The structural model of a pathway contains all the genes and other pathways involved in the pathway functionality under study. Each pathway model component has an associated functional weight. The model of a phenotype contains all the genes, pathways and other phenotypes involved in the phenotype under study development. Each phenotype component has an associated contribution weight, which represents the role of each specific phenotype model component about the total development of a particular phenotype.
Resultados:
Initial experiments have been done with the exomes of patients having “Congenital Disorder of Glycosylation, Type 1A”, including some samples acting as control, revealing differences in the likelihood of developing the phenotype between the “case-samples” and “control-samples” of two, three and four orders of magnitude.
Conclusiones:
This method is able to measure the contribution of each variant, gene and pathway to development of the phenotype in each patient, also showing how the addition of the small effects from different variants in different genes contribute to the development of the phenotype. This method will be able to integrate RNA-seq, Methyl-seq and clinical data, to increase its sensitivity and specificity.
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